In [1]:
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import to_hex
import seaborn as sns
import numpy as np
import os
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.manifold import TSNE
from scipy.cluster.hierarchy import linkage, dendrogram
from scipy.stats import ttest_ind
from statsmodels.stats.multitest import multipletests
In [2]:
df = pd.read_csv('C:/Users/Lympha/Desktop/temp_dir/result_dataframes/pyrosetta_electrostatics_dataframe.csv')
In [3]:
print(df.info)

<bound method DataFrame.info of     Unnamed: 0    pos1:M    pos2:T    pos3:E    pos4:Y    pos5:K    pos6:L  \
0         1A2B -3.537481 -1.205886 -4.196487       NaN -3.822600 -2.120818   
1         1AA9 -0.098174 -0.300505 -0.708706 -1.478823 -3.110961 -0.280144   
2         1AGP -0.389432 -2.014907 -0.608544 -1.789009 -3.985743 -1.788322   
3         1AM4       NaN       NaN       NaN  0.097988       NaN       NaN   
4         1AN0 -0.904729       NaN       NaN       NaN       NaN       NaN   
..         ...       ...       ...       ...       ...       ...       ...   
376       8DNJ -0.432372 -1.779502 -2.275763 -1.960144 -6.052928 -2.169278   
377       8EBZ       NaN       NaN       NaN       NaN       NaN  0.180441   
378       8EZG -0.761445 -1.610530 -0.263310 -1.586195 -3.904273 -2.398022   
379       8F0M       NaN       NaN -0.410603       NaN       NaN       NaN   
380       8IJ9  0.071908  0.190113 -1.375021 -0.426271  0.042569 -2.471542   

       pos7:V    pos8:V    pos9:V  ...  pos180:G  pos181:C  pos182:M  \
0   -1.870031 -0.813876 -1.318342  ...       NaN       NaN       NaN   
1   -1.177681 -1.045136 -1.265456  ...       NaN       NaN       NaN   
2   -2.673340 -0.805669 -1.280177  ...       NaN       NaN       NaN   
3   -0.451689 -0.653154 -1.573362  ...       NaN       NaN       NaN   
4         NaN       NaN       NaN  ...       NaN       NaN       NaN   
..        ...       ...       ...  ...       ...       ...       ...   
376 -2.203326 -1.784165 -1.597457  ...       NaN       NaN       NaN   
377       NaN       NaN       NaN  ...       NaN       NaN       NaN   
378 -2.355439 -1.268634 -1.334235  ...       NaN       NaN       NaN   
379       NaN       NaN       NaN  ...       NaN       NaN       NaN   
380 -2.237245 -1.213751 -1.890804  ...       NaN       NaN       NaN   

     pos183:S  pos184:C  pos185:K  pos186:C  pos187:V  pos188:L  pos189:S  
0         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
1         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
2         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
3         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
4         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
..        ...       ...       ...       ...       ...       ...       ...  
376       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
377       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
378       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
379       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
380       NaN       NaN       NaN       NaN       NaN       NaN       NaN  

[381 rows x 190 columns]>
In [4]:
print(df.head)

<bound method NDFrame.head of     Unnamed: 0    pos1:M    pos2:T    pos3:E    pos4:Y    pos5:K    pos6:L  \
0         1A2B -3.537481 -1.205886 -4.196487       NaN -3.822600 -2.120818   
1         1AA9 -0.098174 -0.300505 -0.708706 -1.478823 -3.110961 -0.280144   
2         1AGP -0.389432 -2.014907 -0.608544 -1.789009 -3.985743 -1.788322   
3         1AM4       NaN       NaN       NaN  0.097988       NaN       NaN   
4         1AN0 -0.904729       NaN       NaN       NaN       NaN       NaN   
..         ...       ...       ...       ...       ...       ...       ...   
376       8DNJ -0.432372 -1.779502 -2.275763 -1.960144 -6.052928 -2.169278   
377       8EBZ       NaN       NaN       NaN       NaN       NaN  0.180441   
378       8EZG -0.761445 -1.610530 -0.263310 -1.586195 -3.904273 -2.398022   
379       8F0M       NaN       NaN -0.410603       NaN       NaN       NaN   
380       8IJ9  0.071908  0.190113 -1.375021 -0.426271  0.042569 -2.471542   

       pos7:V    pos8:V    pos9:V  ...  pos180:G  pos181:C  pos182:M  \
0   -1.870031 -0.813876 -1.318342  ...       NaN       NaN       NaN   
1   -1.177681 -1.045136 -1.265456  ...       NaN       NaN       NaN   
2   -2.673340 -0.805669 -1.280177  ...       NaN       NaN       NaN   
3   -0.451689 -0.653154 -1.573362  ...       NaN       NaN       NaN   
4         NaN       NaN       NaN  ...       NaN       NaN       NaN   
..        ...       ...       ...  ...       ...       ...       ...   
376 -2.203326 -1.784165 -1.597457  ...       NaN       NaN       NaN   
377       NaN       NaN       NaN  ...       NaN       NaN       NaN   
378 -2.355439 -1.268634 -1.334235  ...       NaN       NaN       NaN   
379       NaN       NaN       NaN  ...       NaN       NaN       NaN   
380 -2.237245 -1.213751 -1.890804  ...       NaN       NaN       NaN   

     pos183:S  pos184:C  pos185:K  pos186:C  pos187:V  pos188:L  pos189:S  
0         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
1         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
2         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
3         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
4         NaN       NaN       NaN       NaN       NaN       NaN       NaN  
..        ...       ...       ...       ...       ...       ...       ...  
376       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
377       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
378       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
379       NaN       NaN       NaN       NaN       NaN       NaN       NaN  
380       NaN       NaN       NaN       NaN       NaN       NaN       NaN  

[381 rows x 190 columns]>
In [5]:
metadata_df = pd.read_csv('C:/Users/Lympha/Desktop/temp_dir/result_dataframes/metadata_dataframe.csv')


metadata_df.head()
Out[5]:
Unnamed: 0 Title Structure Details Source Organism Taxonomy ID Abstract Method Resolution Original Number of Models Original Number of Chains ... Number of ILE Number of GLN Number of ASN Number of HIS Number of PHE Number of ASP Number of PRO Number of ARG Number of CYS Number of TRP
0 1A2B HUMAN RHOA COMPLEXED WITH GTP ANALOGUE NaN Homo sapiens 9606 The 2.4-A resolution crystal structure of a do... x-ray diffraction 2.4 1 1 ... 10 5 6 2.0 7 15 11.0 10 5.0 2.0
1 1AA9 HUMAN C-HA-RAS(1-171)(DOT)GDP, NMR, MINIMIZED ... NaN Homo sapiens 9606 The backbone 1H, 13C, and 15N resonances of th... solution nmr NaN 1 1 ... 11 11 4 3.0 5 14 3.0 12 3.0 NaN
2 1AGP THREE-DIMENSIONAL STRUCTURES AND PROPERTIES OF... C-H-RAS P21 PROTEIN MUTANT WITH GLY 12 REPLACE... Homo sapiens 9606 The three-dimensional structures and biochemic... x-ray diffraction 2.3 1 1 ... 11 11 4 3.0 5 15 3.0 11 3.0 NaN
3 1AM4 COMPLEX BETWEEN CDC42HS.GMPPNP AND P50 RHOGAP ... NaN Homo sapiens 9606 Small G proteins transduce signals from plasma... x-ray diffraction 2.7 1 6 ... 8 6 5 2.0 8 11 12.0 5 5.0 1.0
4 1AN0 CDC42HS-GDP COMPLEX NaN Homo sapiens 9606 No DOI found x-ray diffraction 2.8 1 2 ... 8 6 5 2.0 8 11 15.0 6 6.0 1.0

5 rows × 42 columns

In [6]:
plt.figure(figsize=(100,70))
sns.heatmap(df.drop(columns=['Unnamed: 0']), cmap='viridis')
plt.title('Electrostatics on HRAS Experimental Structures')
plt.xlabel('Metric')
plt.ylabel('Structures')
plt.show
Out[6]:
<function matplotlib.pyplot.show(close=None, block=None)>
In [7]:
plt.figure(figsize=(15,10))
sns.histplot(df.drop(columns=['Unnamed: 0']).values.flatten(), bins=50, kde=True)
plt.xlabel('Value')
plt.ylabel('Density')
plt.title('Distribution of Values in Electrostatics dataframe')
plt.show()
In [8]:
nan_percentage = df.isnull().mean() * 100


plt.figure(figsize=(100,70))
sns.barplot(x=nan_percentage.index, y=nan_percentage.values)
plt.xticks(rotation=90)
plt.ylabel('Percentage of NaN values')
plt.title('Percentage of NaN values in each column of Electrostatics dataframe')
plt.show()
In [9]:
merged_nonnorm_df = pd.merge(df, metadata_df[['Unnamed: 0', 'Read Activity Status']], on='Unnamed: 0')

melted_nonnorm = pd.melt(merged_nonnorm_df, id_vars=['Unnamed: 0', 'Read Activity Status'], var_name='Amino Acid Position', value_name='Value')


plt.figure(figsize=(50,40))


sns.violinplot(x="Amino Acid Position", y="Value", hue="Read Activity Status", data=melted_nonnorm, split=True, inner="quart", palette={"active": "red", "inactive": "blue"})


plt.xticks(rotation=90)


plt.title('Violin Plot for All Amino Acid Positions')
plt.xlabel('Amino Acid Position')
plt.ylabel('Value')
plt.legend(title='Read Activity Status')


plt.tight_layout()


plt.show()
In [10]:
protein_codes = df['Unnamed: 0']


feature_df_numeric = df.drop(columns=['Unnamed: 0'])


scaler = StandardScaler()
feature_normalized = scaler.fit_transform(feature_df_numeric)


feature_normalized_df = pd.DataFrame(feature_normalized, columns=feature_df_numeric.columns)


feature_normalized_df.fillna(0, inplace=True)

C:\Users\Lympha\anaconda3\lib\site-packages\sklearn\utils\extmath.py:985: RuntimeWarning: invalid value encountered in true_divide
  updated_mean = (last_sum + new_sum) / updated_sample_count
C:\Users\Lympha\anaconda3\lib\site-packages\sklearn\utils\extmath.py:990: RuntimeWarning: invalid value encountered in true_divide
  T = new_sum / new_sample_count
C:\Users\Lympha\anaconda3\lib\site-packages\sklearn\utils\extmath.py:1020: RuntimeWarning: invalid value encountered in true_divide
  new_unnormalized_variance -= correction ** 2 / new_sample_count
In [11]:
plt.figure(figsize=(100,70))
sns.heatmap(feature_normalized_df, cmap="YlGnBu", cbar_kws={'label': 'Z-score'})
plt.title('Heatmap of Normalized Electrostatics Dataframe')
plt.show()
In [12]:
plt.figure(figsize=(15,10))
sns.histplot(feature_normalized_df.values.flatten(), bins=50, kde=True)
plt.xlabel('Value')
plt.ylabel('Density')
plt.title('Distribution of Values in Electrostatics Dataframe')
plt.show()
In [13]:
merged_df = pd.merge(feature_normalized_df, metadata_df, left_on=protein_codes, right_on="Unnamed: 0")


X = feature_normalized_df
y = metadata_df["Read Activity Status"]
y_factorized = pd.factorize(y)[0]


merged_df.head()
Out[13]:
pos1:M pos2:T pos3:E pos4:Y pos5:K pos6:L pos7:V pos8:V pos9:V pos10:G ... Number of ILE Number of GLN Number of ASN Number of HIS Number of PHE Number of ASP Number of PRO Number of ARG Number of CYS Number of TRP
0 -3.772380 -0.170861 -2.516790 0.000000 0.174061 -0.156413 0.151604 1.066433 0.766768 -0.320586 ... 10 5 6 2.0 7 15 11.0 10 5.0 2.0
1 0.442257 0.792709 0.212057 0.212133 0.582929 2.545591 1.148803 0.770506 0.884470 1.438407 ... 11 11 4 3.0 5 14 3.0 12 3.0 NaN
2 0.085341 -1.031880 0.290424 -0.046576 0.080329 0.331672 -1.005412 1.076936 0.851707 0.020562 ... 11 11 4 3.0 5 15 3.0 11 3.0 NaN
3 0.000000 0.000000 0.000000 1.527265 0.000000 0.000000 2.194458 1.272097 0.199209 0.000000 ... 8 6 5 2.0 8 11 12.0 5 5.0 1.0
4 -0.546122 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.153206 ... 8 6 5 2.0 8 11 15.0 6 6.0 1.0

5 rows × 231 columns

In [14]:
melted_data = pd.melt(X.iloc[:, :len(feature_normalized_df.columns)], value_vars=X.iloc[:, :len(feature_normalized_df.columns)].columns)
melted_data['Activity Status'] = np.tile(y, len(X.columns[:len(feature_normalized_df.columns)]))


plt.figure(figsize=(50,40))
sns.violinplot(x="variable", y="value", hue="Activity Status", data=melted_data, split=True, inner="quart", palette={"active": "red", "inactive": "blue"})
plt.xticks(rotation=90)
plt.title('Violin Plot for All Amino Acid Positions')
plt.xlabel('Amino Acid Position')
plt.ylabel('Value')
plt.legend(title='Activity Status')
plt.tight_layout()
plt.show()
In [15]:
correlation_matrix = X.iloc[:, :len(feature_normalized_df.columns)].corr()


correlation_with_target = X.iloc[:, :len(feature_normalized_df.columns)].apply(lambda x: x.corr(pd.Series(y_factorized)))


plt.figure(figsize=(100, 70))
sns.heatmap(correlation_matrix, cmap="coolwarm", vmin=-1, vmax=1, cbar_kws={'label': 'Correlation'})
plt.title('Correlation Matrix of Amino Acid Positions')
plt.show()


correlation_with_target_abs = correlation_with_target.abs().sort_values(ascending=False)
correlation_with_target_sorted = correlation_with_target[correlation_with_target_abs.index]
correlation_with_target_sorted.head(10)
Out[15]:
pos33:D     0.336839
pos35:T     0.310621
pos60:G     0.211875
pos56:L    -0.210991
pos143:E   -0.208494
pos63:E     0.196428
pos37:E     0.195805
pos106:S    0.189168
pos70:Q     0.187856
pos163:I    0.180984
dtype: float64
In [16]:
linked = linkage(feature_normalized, method='ward')


color_map = {
    "active": "red",
    "inactive": "blue"
}


labels = df["Unnamed: 0"].values


plt.figure(figsize=(20,15))
dendro_data = dendrogram(linked, orientation='top', distance_sort='descending', show_leaf_counts=True, labels=labels)


ax = plt.gca()
xlbls = ax.get_xmajorticklabels()
for lbl in xlbls:
    structure_id = lbl.get_text()
    color = color_map[merged_df[merged_df["Unnamed: 0"] == structure_id]["Read Activity Status"].values[0]]
    lbl.set_color(color)

plt.title('Full Hierarchical Clustering Dendrogram for Electrostatics (Colored by Activation Status)')
plt.xlabel('Protein Structure Names')
plt.ylabel('Distance (Ward)')
plt.xticks(rotation=90)  # Rotate x-axis labels for better readability
plt.show()
In [17]:
merged_df = pd.merge(feature_normalized_df, metadata_df, left_on=protein_codes, right_on="Unnamed: 0")


X = feature_normalized_df
y = merged_df["Read Activity Status"]


merged_df.head()
Out[17]:
pos1:M pos2:T pos3:E pos4:Y pos5:K pos6:L pos7:V pos8:V pos9:V pos10:G ... Number of ILE Number of GLN Number of ASN Number of HIS Number of PHE Number of ASP Number of PRO Number of ARG Number of CYS Number of TRP
0 -3.772380 -0.170861 -2.516790 0.000000 0.174061 -0.156413 0.151604 1.066433 0.766768 -0.320586 ... 10 5 6 2.0 7 15 11.0 10 5.0 2.0
1 0.442257 0.792709 0.212057 0.212133 0.582929 2.545591 1.148803 0.770506 0.884470 1.438407 ... 11 11 4 3.0 5 14 3.0 12 3.0 NaN
2 0.085341 -1.031880 0.290424 -0.046576 0.080329 0.331672 -1.005412 1.076936 0.851707 0.020562 ... 11 11 4 3.0 5 15 3.0 11 3.0 NaN
3 0.000000 0.000000 0.000000 1.527265 0.000000 0.000000 2.194458 1.272097 0.199209 0.000000 ... 8 6 5 2.0 8 11 12.0 5 5.0 1.0
4 -0.546122 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.153206 ... 8 6 5 2.0 8 11 15.0 6 6.0 1.0

5 rows × 231 columns

In [18]:
pca = PCA(n_components=2)
principal_components = pca.fit_transform(X.iloc[:, :len(feature_normalized_df.columns)])


pca_df = pd.DataFrame(data=principal_components, columns=['Principal Component 1', 'Principal Component 2'])
pca_df['Activity Status'] = y


explained_variance = pca.explained_variance_ratio_


plt.figure(figsize=(10, 7))
sns.scatterplot(x='Principal Component 1', y='Principal Component 2', hue='Activity Status', data=pca_df, palette={"active": "red", "inactive": "blue"})


plt.xlabel(f'Principal Component 1 ({explained_variance[0]*100:.2f}%)')
plt.ylabel(f'Principal Component 2 ({explained_variance[1]*100:.2f}%)')

plt.title('2D PCA of Amino Acid Positions')
plt.show()
In [19]:
pca_3d = PCA(n_components=3)
principal_components_3d = pca_3d.fit_transform(X)


pca_df_3d = pd.DataFrame(data=principal_components_3d, columns=['Principal Component 1', 'Principal Component 2', 'Principal Component 3'])
pca_df_3d['Activity Status'] = y


colors = {'inactive': 'blue', 'active': 'red'}


explained_variance_3d = pca_3d.explained_variance_ratio_


fig = plt.figure(figsize=(15,10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(pca_df_3d['Principal Component 1'], pca_df_3d['Principal Component 2'], pca_df_3d['Principal Component 3'], c=pca_df_3d["Activity Status"].map(colors), s=50, label=pca_df_3d["Activity Status"].unique())
ax.set_xlabel(f'Principal Component 1 ({explained_variance_3d[0]*100:.2f}%)')
ax.set_ylabel(f'Principal Component 2 ({explained_variance_3d[1]*100:.2f}%)')
ax.set_zlabel(f'Principal Component 3 ({explained_variance_3d[2]*100:.2f}%)')
ax.set_title('3D PCA of Amino Acid Positions')
legend_handles = [plt.Line2D([0], [0], marker='o', color='w', label=status, markersize=10, markerfacecolor=colors[status]) for status in colors]
ax.legend(handles=legend_handles, title='Activity Status')

plt.show()
In [20]:
tsne = TSNE(n_components=2, random_state=1)
tsne_2d = tsne.fit_transform(X)


tsne_df = pd.DataFrame(data=tsne_2d, columns=['t-SNE 1', 't-SNE 2'])
tsne_df['Activity Status'] = y


plt.figure(figsize=(10, 7))
sns.scatterplot(x='t-SNE 1', y='t-SNE 2', hue='Activity Status', data=tsne_df, palette={"active": "red", "inactive": "blue"})
plt.title('t-SNE Projection of Electrostatics Data')
plt.show()

C:\Users\Lympha\anaconda3\lib\site-packages\sklearn\manifold\_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.
  warnings.warn(
C:\Users\Lympha\anaconda3\lib\site-packages\sklearn\manifold\_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.
  warnings.warn(
In [21]:
label_encoder = LabelEncoder()
y_factorized = label_encoder.fit_transform(y)


rf_clf = RandomForestClassifier(n_estimators=100, random_state=1)
rf_clf.fit(X.iloc[:, :len(feature_normalized_df.columns)], y_factorized)


feature_importances = rf_clf.feature_importances_


importance_df = pd.DataFrame({
    'Amino Acid Position': X.columns[:len(feature_normalized_df.columns)],
    'Importance': feature_importances
})


sorted_importance_df = importance_df.sort_values(by='Importance', ascending=False)


top_n = 15
selected_aminoacids = sorted_importance_df['Amino Acid Position'][:top_n]
sorted_importance_df
Out[21]:
Amino Acid Position Importance
59 pos60:G 0.034593
34 pos35:T 0.031828
32 pos33:D 0.026843
69 pos70:Q 0.020559
58 pos59:A 0.020305
... ... ...
175 pos176:E 0.000000
174 pos175:D 0.000000
173 pos174:P 0.000000
172 pos173:P 0.000000
188 pos189:S 0.000000

189 rows × 2 columns

In [22]:
top_features = sorted_importance_df.head(top_n)


colors = cm.Set2(np.linspace(0, 1, top_n))


plt.figure(figsize=(10, 8))
bars = plt.barh(top_features['Amino Acid Position'], top_features['Importance'], color=colors)
plt.gca().invert_yaxis()  # to have the most important feature at the top
plt.title('Top {} Amino Acid Positions by Importance in Electrostatic Profile'.format(top_n))
plt.xlabel('Importance')
plt.ylabel('Amino Acid Position')
plt.tight_layout()
plt.show()
In [23]:
colors = cm.Set2(np.linspace(0, 1, len(selected_aminoacids)))


hex_colors = [to_hex(color) for color in colors]


color_dict = dict(zip(selected_aminoacids, hex_colors))


plt.figure(figsize=(15, 10))
for position in selected_aminoacids:
    sns.distplot(X[position], label=position, hist=False, color=color_dict[position])

plt.title('Distribution of Selected Amino Acid Positions')
plt.xlabel('Value')
plt.ylabel('Density')
plt.legend()
plt.show()

C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
C:\Users\Lympha\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).
  warnings.warn(msg, FutureWarning)
In [24]:
selected_correlation_matrix = correlation_matrix.loc[selected_aminoacids, selected_aminoacids]


plt.figure(figsize=(10, 8))
sns.heatmap(selected_correlation_matrix, cmap="coolwarm", annot=True, vmin=-1, vmax=1, cbar_kws={'label': 'Correlation'})
plt.title('Correlation Matrix of Important Amino Acid Positions')
plt.show()
In [25]:
plt.figure(figsize=(10, 6))
for idx, position in enumerate(selected_aminoacids):
    plt.subplot(3, 5, idx+1)
    sns.boxplot(x=y_factorized, y=X[position])
    plt.title(f'{position}:{round(sorted_importance_df.iloc[idx, sorted_importance_df.columns.get_loc("Importance")], 4)}')
    plt.xlabel('Activity Status')
    plt.ylabel('Value')

plt.tight_layout()
plt.show()
In [26]:
melted_data_selected = pd.melt(X[selected_aminoacids], value_vars=selected_aminoacids)
melted_data_selected['Activity Status'] = np.tile(y, len(selected_aminoacids))


plt.figure(figsize=(10, 6))
sns.violinplot(x="variable", y="value", hue="Activity Status", data=melted_data_selected, split=True, inner="quart", palette={"active": "red", "inactive": "blue"})
plt.title('Violin Plot for Selected Amino Acid Positions')
plt.xlabel('Amino Acid Position')
plt.ylabel('Value')
plt.legend(title='Activity Status')
plt.tight_layout()
plt.show()
In [27]:
active_data = X[y == "active"][selected_aminoacids]
inactive_data = X[y == "inactive"][selected_aminoacids]


t_stats = []
p_values = []

for position in selected_aminoacids:
    t_stat, p_value = ttest_ind(active_data[position], inactive_data[position])
    t_stats.append(t_stat)
    p_values.append(p_value)


t_test_results = pd.DataFrame({
    'Amino Acid Position': selected_aminoacids,
    'T-Statistic': t_stats,
    'P-Value': p_values
})

t_test_results
Out[27]:
Amino Acid Position T-Statistic P-Value
59 pos60:G 4.220587 3.052990e-05
34 pos35:T 6.361851 5.750484e-10
32 pos33:D 6.964540 1.462944e-11
69 pos70:Q 3.723447 2.263544e-04
58 pos59:A 3.363153 8.491286e-04
36 pos37:E 3.887159 1.197191e-04
31 pos32:Y 2.173189 3.038484e-02
9 pos10:G -2.107621 3.572009e-02
27 pos28:F 0.916894 3.597814e-01
37 pos38:D -3.406682 7.280216e-04
15 pos16:K 3.141064 1.815577e-03
142 pos143:E -4.150155 4.105972e-05
98 pos99:Q -0.363568 7.163832e-01
79 pos80:C -0.394587 6.933700e-01
55 pos56:L -4.202161 3.300388e-05
In [32]:
colors = cm.Set2(np.linspace(0, 1, top_n))


fig, ax1 = plt.subplots(figsize=(12, 8))


bars = ax1.bar(t_test_results['Amino Acid Position'], t_test_results['T-Statistic'], color=colors, label='T-Statistic')


ax2 = ax1.twinx()
ax2.scatter(t_test_results['Amino Acid Position'], t_test_results['P-Value'], color='red', marker='o', label='P-Value')
ax2.axhline(y=0.05, color='black', linestyle='--')  # significance threshold


ax2.set_ylim(0, 1)


ax1.set_ylabel('T-Statistic')
ax2.set_ylabel('P-Value', color='red')
ax1.set_xlabel('Amino Acid Position')
ax1.set_title(f'T-Test Results for Top {top_n} Amino Acid Positions')
ax1.legend(loc='upper left')
ax2.legend(loc='upper right')

plt.tight_layout()
plt.show()
In [29]:
bonferroni_corrected_pvalues = multipletests(t_test_results['P-Value'], method='bonferroni')[1]


fdr_corrected_pvalues = multipletests(t_test_results['P-Value'], method='fdr_bh')[1]


t_test_results['Bonferroni Corrected P-Value'] = bonferroni_corrected_pvalues
t_test_results['FDR Corrected P-Value'] = fdr_corrected_pvalues

t_test_results
Out[29]:
Amino Acid Position T-Statistic P-Value Bonferroni Corrected P-Value FDR Corrected P-Value
59 pos60:G 4.220587 3.052990e-05 4.579485e-04 1.231792e-04
34 pos35:T 6.361851 5.750484e-10 8.625726e-09 4.312863e-09
32 pos33:D 6.964540 1.462944e-11 2.194417e-10 2.194417e-10
69 pos70:Q 3.723447 2.263544e-04 3.395316e-03 4.850451e-04
58 pos59:A 3.363153 8.491286e-04 1.273693e-02 1.415214e-03
36 pos37:E 3.887159 1.197191e-04 1.795787e-03 2.992978e-04
31 pos32:Y 2.173189 3.038484e-02 4.557726e-01 4.143387e-02
9 pos10:G -2.107621 3.572009e-02 5.358013e-01 4.465011e-02
27 pos28:F 0.916894 3.597814e-01 1.000000e+00 4.151324e-01
37 pos38:D -3.406682 7.280216e-04 1.092032e-02 1.365040e-03
15 pos16:K 3.141064 1.815577e-03 2.723366e-02 2.723366e-03
142 pos143:E -4.150155 4.105972e-05 6.158959e-04 1.231792e-04
98 pos99:Q -0.363568 7.163832e-01 1.000000e+00 7.163832e-01
79 pos80:C -0.394587 6.933700e-01 1.000000e+00 7.163832e-01
55 pos56:L -4.202161 3.300388e-05 4.950582e-04 1.231792e-04
In [30]:
t_test_results.to_clipboard()
In [31]:
colors = cm.Set2(np.linspace(0, 1, top_n))


fig, ax1 = plt.subplots(figsize=(12,8))


bars = ax1.bar(t_test_results['Amino Acid Position'], t_test_results['T-Statistic'], color=colors, label='T-Statistic')


ax2 = ax1.twinx()
ax2.scatter(t_test_results['Amino Acid Position'], t_test_results['FDR Corrected P-Value'], color='red', marker='o', label='FDR Corrected P-Value')
ax2.axhline(y=0.05, color='black', linestyle='--')  # significance threshold


ax2.set_ylim(0, 1)


ax1.set_ylabel('T-Statistic')
ax2.set_ylabel('P-Value', color='red')
ax1.set_xlabel('Amino Acid Position')
ax1.set_title(f'T-Test Results for Top {top_n} Amino Acid Positions in Electrostatic Profile')
ax1.legend(loc='upper left')
ax2.legend(loc='upper right')

plt.tight_layout()
plt.show()